****PKAT PKO misconduct replication models

***CAT1_SEA_MIL Models, reported in Table 1

*Model 1
nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 2								
nbreg CAT1_SEA_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 3
nbreg CAT1_SEA_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 4
nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 5
nbreg CAT1_SEA_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 6
nbreg CAT1_SEA_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 7
nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

*Model 8
nbreg CAT1_SEA_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

*Model 9
nbreg CAT1_SEA_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)



***CAT1_MIL Models, reported in Table 2

*Model 10
nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 11							
nbreg CAT1_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 12
nbreg CAT1_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 13
nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 14
nbreg CAT1_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 15
nbreg CAT1_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 16
nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

*Model 17
nbreg CAT1_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

*Model 18
nbreg CAT1_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)



***CAT2_MIL Models, reported in Table 3

*Model 19
nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 20							
nbreg CAT2_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 21
nbreg CAT2_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

*Model 22
nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 23
nbreg CAT2_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 24
nbreg CAT2_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 25
nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

*Model 26
nbreg CAT2_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

*Model 27
nbreg CAT2_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)



***Figures

**Note: Reproduction of these figures requires installation of the Travis Braidwood CLEAR-PLOT package available at http://travisbraidwood.altervista.org/dataverse.html.

*Figure 1. Expected Category 1 allegations among PKO military forces including SEAs, by the weighted Political Terror Scores of the PKO military force. 
estsimp nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)
preserve
local a =0 
setx mean 
setx PTS_MISSION_MIL (`a') 
simqi, level(95)
macro list
scalar list 
postutil clear
postfile mypost prediction upper lower using simresults, replace 
noisily display "start"
set obs 10000 
while `a' <= 3  {
qui simqi , level(95)
scalar prediction= Pr
scalar upper = PrU
scalar lower = PrL
post mypost (prediction) (upper) (lower)
scalar drop prediction upper lower
local a = `a'+.005
setx PTS_MISSION_MIL (`a') 
display "." _c 
}
display ""
postclose mypost 
use simresults, clear 
sum
gen MV = 0+.005*(_n-1) 
gsort prediction upper lower -MV 
graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 


*Figure 2. Expected Category 1 allegations among PKO military forces including SEAs, by the weighted Corruption Scores of the PKO military force.
**Generate the graph using Model 6, above.	
estsimp nbreg CAT1_SEA_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)
preserve
local a =-1 
setx mean 
setx CORRUPTION_MIL (`a') 
simqi, level(95)
macro list
scalar list 
postutil clear
postfile mypost prediction upper lower using simresults, replace 
noisily display "start"
set obs 10000 
while `a' <= 1  {
qui simqi , level(95)
scalar prediction= Pr
scalar upper = PrU
scalar lower = PrL
post mypost (prediction) (upper) (lower)
scalar drop prediction upper lower
local a = `a'+.005
setx CORRUPTION_MIL (`a') 
display "." _c 
}
display ""
postclose mypost 
use simresults, clear 
sum
gen MV = 0+.005*(_n-1) 
gsort prediction upper lower -MV 
graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 
		

*Figure 3. Expected Category 1 allegations among PKO military forces excluding SEAs, by the weighted Political Terror Scores of the PKO military force.	
estsimp nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)
preserve
local a =0 
setx mean 
setx PTS_MISSION_MIL (`a') 
simqi, level(95)
macro list
scalar list 
postutil clear
postfile mypost prediction upper lower using simresults, replace 
noisily display "start"
set obs 10000 
while `a' <= 4  {
qui simqi , level(95)
scalar prediction= Pr
scalar upper = PrU
scalar lower = PrL
post mypost (prediction) (upper) (lower)
scalar drop prediction upper lower
local a = `a'+.005
setx PTS_MISSION_MIL (`a') 
display "." _c 
}
display ""
postclose mypost 
use simresults, clear 
sum
gen MV = 0+.005*(_n-1) 
gsort prediction upper lower -MV 
graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 


*Figure 4. Expected Category 1 allegations among PKO military forces excluding SEAs, by the weighted Corruption Scores of the PKO military force.
**Generate the graph using Model 15, above.	
estsimp nbreg CAT1_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)
preserve
local a =-1 
setx mean 
setx CORRUPTION_MIL (`a') 
simqi, level(95)
macro list
scalar list 
postutil clear
postfile mypost prediction upper lower using simresults, replace 
noisily display "start"
set obs 10000 
while `a' <= 1  {
qui simqi , level(95)
scalar prediction= Pr
scalar upper = PrU
scalar lower = PrL
post mypost (prediction) (upper) (lower)
scalar drop prediction upper lower
local a = `a'+.005
setx CORRUPTION_MIL (`a') 
display "." _c 
}
display ""
postclose mypost 
use simresults, clear 
sum
gen MV = 0+.005*(_n-1) 
gsort prediction upper lower -MV 
graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 


*Figure 5. Expected Category 2 allegations among PKO military, by the weighted Political Terror Scores of the PKO military force.	
estsimp nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)
preserve
local a =0 
setx mean 
setx PTS_MISSION_MIL (`a') 
simqi, level(95)
macro list
scalar list 
postutil clear
postfile mypost prediction upper lower using simresults, replace 
noisily display "start"
set obs 10000 
while `a' <= 3  {
qui simqi , level(95)
scalar prediction= Pr
scalar upper = PrU
scalar lower = PrL
post mypost (prediction) (upper) (lower)
scalar drop prediction upper lower
local a = `a'+.005
setx PTS_MISSION_MIL (`a') 
display "." _c 
}
display ""
postclose mypost 
use simresults, clear 
sum
gen MV = 0+.005*(_n-1) 
gsort prediction upper lower -MV 
graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 


*Figure 6. Expected Category 2 allegations among PKO military, by the weighted Corruption Scores of the PKO military force.	
**Generate the graph using Model 24, above.	
estsimp nbreg CAT2_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)
preserve
local a =-1
setx mean 
setx CORRUPTION_MIL (`a') 
simqi, level(95)
macro list
scalar list 
postutil clear
postfile mypost prediction upper lower using simresults, replace 
noisily display "start"
set obs 10000 
while `a' <= 1  {
qui simqi , level(95)
scalar prediction= Pr
scalar upper = PrU
scalar lower = PrL
post mypost (prediction) (upper) (lower)
scalar drop prediction upper lower
local a = `a'+.005
setx CORRUPTION_MIL (`a') 
display "." _c 
}
display ""
postclose mypost 
use simresults, clear 
sum
gen MV = 0+.005*(_n-1) 
gsort prediction upper lower -MV 
graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 


**Online Appendix A
*Figure A-1: Distribution of CAT1 allegations including SEAs (Models 1-9)
hist CAT1_SEA_MIL, freq

*Figure A-2: Distribution of CAT1 allegations excluding SEAs (Models 10-18)
hist CAT1_MIL, freq

*Figure A-3: Distribution of CAT2 allegations (Models 19-27)
hist CAT2_MIL, freq

*Figure A-4: Distribution of Weighted Political Terror Scores for PKO military forces
hist PTS_MISSION_MIL, freq

*Figure A-5: Distribution of Weighted Corruption Scores for PKO military forces
hist CORRUPTION_MIL, freq

*Summary statistics (Table A-3)
summ CAT1_SEA_MIL if YEAR >2008 & YEAR <2017

summ CAT1_MIL if YEAR >2008 & YEAR <2017

summ CAT2_MIL if YEAR >2008 & YEAR <2017

summ PTS_MISSION_MIL if YEAR >2008 & YEAR <2017

summ DEMOCRATIC_MIL if YEAR >2008 & YEAR <2017

summ CORRUPTION_MIL if YEAR >2008 & YEAR <2017

summ GDP_MISSION_MIL if YEAR >2008 & YEAR <2017

summ GENDER_MIL if YEAR >2008 & YEAR <2017

summ FORCE_SIZE if YEAR >2008 & YEAR <2017

summ FORCE_DENSITY if YEAR >2008 & YEAR <2017

summ PKO_FATALITIES_TOTAL if YEAR >2008 & YEAR <2017

summ MANDATE3 if YEAR >2008 & YEAR <2017

summ KM2_country if YEAR >2008 & YEAR <2017

summ ln_KM2_country if YEAR >2008 & YEAR <2017

summ POP_DENSITY if YEAR >2008 & YEAR <2017

summ GDP_HOST if YEAR >2008 & YEAR <2017

*Table A-4: PTS / PTS-Amnesty / PTS-State Comparisons of models depicted in Figures 1, 3, and 5
*Model 4
nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 4-Amnesty
nbreg CAT1_SEA_MIL PTSA_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 4-State
nbreg CAT1_SEA_MIL PTSS_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 13
nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 13-Amnesty
nbreg CAT1_MIL PTSA_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 13-State
nbreg CAT1_MIL PTSS_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 22
nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 22-Amnesty
nbreg CAT2_MIL PTSA_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Model 22-State
nbreg CAT2_MIL PTSS_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

*Table A-5: Summary statistics for PTS & Corruption by Mission

summ PTS_MISSION_MIL if YEAR >2008 & YEAR <2017 & ID ==1

summ CORRUPTION_MIL if YEAR >2008 & YEAR <2017 & ID ==1

*and so forth, across mission IDs. The MISSION variable gives PKO mission names and locations.







